The present invention relates to methods of characterization of rock strata in underground mining operations. More specifically, this invention relates to real-time methods employing neural networks for assessing the relative strength of rock strata, and, thus, relative risk of roof collapse or failure during roof bolting and similar operations.
Currently there are about 2,000 underground mines operating in the United States,. including about 1,200 to 1,400 coal mines, about 500 to 600 mineral mines, and about 100 stone mines. Roof bolting is an essential, although extremely hazardous, operation in underground mines to maintain the integrity of a horizontal mine shaft and to help prevent the roof of the mine from collapsing during or after the earth and desired mineral or product has been excavated from the shaft end. Placement of roof bolts (generally about 1 to 3 meters long) is used to reinforce the rock and to significantly enhance the safety of later miners working in the mine environment.
The actual workers drilling and placing the roof bolts are, however, exposed to the significant risk of roof collapse or failure during the bolting operations. As the mine face is extended, a bolting machine first drills holes in the passageway roof and then inserts and sets bolts into the mine roof to keep the roof from collapsing. In order to improve safety, some mining machines have integrated the roof bolting apparatus into the continuous mining machines, thereby reducing the risk of roof collapse. Roof bolting apparatus generally incorporate roof support members to further support the roof during installation of the bolts. Since roof bolting is one of the most dangerous operations in all of underground mining, roof bolters that work ahead of the continuous miner are also being developed. These pre-mining bolters drill into the seam to be mined and insert bolts at this early stage, thereby greatly reducing the risk of roof collapse. These newer roof bolter units may incorporate contemporary robotics technology. In spite of these safety precautions and improvements, the operators of the roof bolting machines and their helpers still are exposed to significant risk; in fact, even with these advances, the process of drilling and bolting the roof is currently one the most dangerous jobs in underground mining. Approximately 1,000 accidents with injuries occur each year in the United States which can be attributed to roof bolting operations.
It would be desirable, therefor, to provide additional methods to increase the safety of underground miners, especially those involved in drilling and placement of roof bolts. It would also be desirable to provide methods which allow real-time or near real-time characterization of the roof strata, including relative strength of the roof strata, as the roof bolt holes are being drilled. It would also be desirable to provide methods and monitoring systems that can be used with conventional roof drilling and bolting machines that can assess the integrity of a mine roof and provide real time warning to the roof drill operator when a weak layer is encountered in the rock strata. The present invention employs neural network techniques to provide such methods and devices. Thus, measurements taken while roof bolt bore is being drilled can be converted to suitably scaled features which allows the various layers of rock strata encountered in the drilling operation to be classified as to relative strength using a neural network. Suitable warning devices can be activated as the drilling progresses if weak or otherwise unsafe strata are encountered.
The present invention provides methods for the characterization of rock strata in underground mining operations during drilling operations. More specifically, this invention provides real-time or near real-time methods using neural networks for assessing the relative strength of rock strata, and, thus, relative risk of roof collapse or failure during roof bolting and similar operations. Using the data generated, real-time or near real-time decisions can be made regarding the relative strength of the rock strata to provide bolter operators and other workers warning of questionable rock layers. Using this information, modification of support and/or roof bolting strategies can be made in near real-time. In addition, advanced warning of potentially unsafe roof conditions can be generated. The ability to provide near real-time data and/or warnings regarding the rock strata and drilling operations is especially important since the process of drilling and bolting the roof is currently one of the most dangerous jobs in underground mining. By using the present monitoring system on a roof drill to assess the integrity of a mine roof, a roof drill operator could be warned when a weak layer is encountered. Such a warning could make the difference between life and death for the operator.
For purposes of this invention, xe2x80x9creal-timexe2x80x9d or xe2x80x9cnear real-timexe2x80x9d determinations or processes are meant to include relatively short time frames such that the relevant information being gathered can be converted into useful and predictive output information during the actual drilling operation so as to allow the operator to modify his or her actions based on the information being supplied. In other words, the delay from the time the data is collected during the drilling operation to the time in which the results of the neural network classification system are available to the operator is relatively short (i.e., preferably within about 1 minute, more preferably within about 10 seconds, and most preferably within about a second).
A cross-section of a typical mine roof and various types of roof support, including bolts, are shown in FIG. 1. As can be seen, mine roof structure can include numerous types or layers of rock. The roof structure can significantly change between roof bolt locations. Thus, an operator drilling roof bolt holds can encounter very different rock strata. Thus, it would be desirable to provide real-time or near real-time data regarding the stability and relative strength of the rock strata encountered during drilling to allow the operator to take the necessary safety precautions (including both short- and long-term precautions) as soon as an unstable situation arises.
The present invention utilizes neural network technology in order to classify mine roof strata in terms of, for example, relative strength. That is, measurements taken while a layer is being drilled can be used to compute the specific energy input and convert these data to suitably scaled features. A neural network is then used to classify the strength of the layer. The neural network can be trained using data of known rock strata classifications prior to using it to classify new measurements. Data from actual drilling operations can be used to upgrade and/or improve the recognition or classification of rock strata by the neural network.
One object of the present invention is to provide a method for determining and analyzing, in near real-time, the relative strength of rock strata during drilling operations in an underground mine, said method comprising
(1) collecting data from a plurality of sensors monitoring a rock drill during roof bolt drilling operations;
(2) converting the data to computer readable input data using transducers coupled with the plurality of sensors in near real-time;
(3) analyzing the computer readable input data in near real-time using a neural network analyzer to determine relative strength and classification of the rock strata encountered by the rock drill; and
(4) providing an output signal detailing the relative strength and classification of the rock strata encountered by the rock drill in near real-time.
Another object of the invention is to provide a method for determining and analyzing, in near real-time, the relative strength of rock strata during drilling operations, said method comprising
(1) collecting data from a plurality of sensors monitoring a rock drill during drilling operations;
(2) converting the data to computer readable input data using transducers coupled with the plurality of sensors in near real-time;
(3) analyzing the computer readable input data in near real-time using a neural network analyzer to determine relative strength and classification of the rock strata encountered by the rock drill; and
(4) providing an output signal detailing the relative strength and classification of the rock strata encountered by the rock drill in near real-time.
Still another object of the present invention is to provide a system for determining and analyzing, in near real-time, the relative strength of rock strata during drilling operations in an underground mine, said system comprising
(1) a plurality of sensors for monitoring and collecting a plurality of subsets of data from a rock drill, wherein the plurality of the subsets of data correspond to layers of rock encountered by the rock drill during roof bolt drilling operations;
(2) transducers coupled with the plurality of sensors to convert the subsets of data from the rock drill to computer readable data in near real-time;
(3) a computer system to accept and analyze the computer readable data in near real-time using a neural network analyzer to determine relative strength and classification of the rock strata encountered by the rock drill; and
(4) an output device coupled to the computer system to provide an output signal detailing the relative strength and classification of the rock strata encountered by the rock drill in near real-time. Preferably, the output device includes both audio and visual signaling capabilities to warn the rock drill operator and other workers in the area if unstable and unsafe rock strata are encountered so that the operator and other workers can take appropriate actions in near retal-time.
These and other objects and advantages of the present invention will be apparent to those skilled in the art upon a consideration of the present specification.